# Thermodynamic Computing via Autonomous Quantum Thermal Machines

arXiv: https://arxiv.org/abs/2308.15905 · HTML: https://mlsanity.com/paper/2308.15905/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 111 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Self-learning Machines based on Hamiltonian Echo Backpropagation](https://mlsanity.com/paper/2103.04992/) (2021)
- [Thermodynamic AI and the fluctuation frontier](https://mlsanity.com/paper/2302.06584/) (2023)
- [Thermodynamic Computing](https://mlsanity.com/paper/1911.01968/) (2019)
- [Thermodynamic Neural Network](https://mlsanity.com/paper/1906.01678/) (2019)
- [Thermodynamic Machine Learning through Maximum Work Production](https://mlsanity.com/paper/2006.15416/) (2020)

## Built on by (most cited first, 5 of 5)

- [Thermodynamic Computing System for AI Applications](https://mlsanity.com/paper/2312.04836/) (2023)
- [Thermodynamic Natural Gradient Descent](https://mlsanity.com/paper/2405.13817/) (2024)
- [Nonlinear thermodynamic computing out of equilibrium](https://mlsanity.com/paper/2412.17183/) (2024)
- [Training thermodynamic computers by gradient descent](https://mlsanity.com/paper/2509.15324/) (2025)
- [Thermodynamic Bayesian Inference](https://mlsanity.com/paper/2410.01793/) (2024)

## Abstract (arXiv)

We develop a physics-based model for classical computation based on autonomous quantum thermal machines. These machines consist of few interacting quantum bits (qubits) connected to several environments at different temperatures. Heat flows through the machine are here exploited for computing. The process starts by setting the temperatures of the environments according to the logical input. The machine evolves, eventually reaching a non-equilibrium steady state, from which the output of the computation can be determined via the temperature of an auxilliary finite-size reservoir. Such a machine, which we term a ``thermodynamic neuron'', can implement any linearly-separable function, and we discuss explicitly the cases of NOT, 3-MAJORITY and NOR gates. In turn, we show that a network of thermodynamic neurons can perform any desired function. We discuss the close connection between our model and artificial neurons (perceptrons), and argue that our model provides an alternative physics-based analogue implementation of neural networks, and more generally a platform for thermodynamic computing.